Image blur kernel classification and parameter estimation are critical for blind image deblurring. Current dominant approaches use handcrafted blur features that are optimized for a certain type of blur, which is not applicable in real blind deconvolution application when the Point Spread Function (PSF) of the blur is unknown. In this paper, a Twostage system using Deep Neural Network (DNN) and...

An interactive deblurring technique to restore a motion blurred image is proposed in this paper. Segment based semiautomated restoration method is proposed using an error gradient descent iterative algorithm. In this approach, segments are automatically detected which are the best representatives of motion blur. Then the decimal parameters of the blur kernel are interactively derived; with exte...

This paper proposes a simple, accurate, and robust approach to single image nonparametric blind Super-Resolution (SR). This task is formulated as a functional to be minimized with respect to both an intermediate super-resolved image and a nonparametric blur-kernel. The proposed approach includes a convolution consistency constraint which uses a non-blind learning-based SR result to better guide...

Blind image restoration algorithms for motion blur have been deeply researched in the past years. Although great progress has been made, blurred images containing large blur and rich, small details still cannot be restored perfectly. To deal with these problems, we present a robust image restoration algorithm for motion blur of general image sensors in this paper. Firstly, we propose a self-ada...

Blind image deblurring algorithms have been improving steadily in the past years. Most state-of-the-art algorithms, however, still cannot perform perfectly in challenging cases, especially in large blur setting. In this paper, we focus on how to estimate a good blur kernel from a single blurred image based on the image structure. We found that image details caused by blur could adversely affect...

Optimal missing data estimation algorithms including deblurring and denoising are designed to restore images captured from large CCD sensor arrays using butting technique, where 1 to 2 columns of data are missed at the butting edge. We developed consistency method with separable deblurring to estimate the missing data. This method converts an ill-posed restoration problem into a well-posed one ...

We address the problem of blind motion deblurring from a single image, caused by a few moving objects. In such situations only part of the image may be blurred, and the scene consists of layers blurred in different degrees. Most of of existing blind deconvolution research concentrates at recovering a single blurring kernel for the entire image. However, in the case of different motions, the blu...

In the manuscript, we discuss the blur kernel measurement giving the ground truth kernel and propose an effective kernel similarity (KS) metric. In this section, we provide examples and more results to justify the proposed kernel similarity metric in Section 3 of the manuscript. A good metric for kernel similarity should be shift and range (i.e., the size of kernel) invariant. Figure 1 shows th...

We present a deblurring method for scenes with occluding objects using a carefully designed layered blur model. Layered blur model is frequently used in the motion deblurring problem to handle locally varying blurs, which is caused by object motions or depth variations in a scene. However, conventional models have a limitation in representing the layer interactions occurring at occlusion bounda...